ML project structure in ML Python - Model Pipeline Trace

Model Pipeline - ML project structure

This ML project structure organizes the steps from raw data to model predictions. It helps keep the work clear and easy to follow.

Data Flow - 7 Stages

1Raw Data Collection

N/A→Gather data from sources like files, databases, or sensors→1000 rows x 10 columns

CSV file with 1000 rows and 10 columns of customer info

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2Data Cleaning

1000 rows x 10 columns→Remove missing values and fix errors→980 rows x 10 columns

Dropped 20 rows with missing age values

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3Feature Engineering

980 rows x 10 columns→Create new features and select important ones→980 rows x 12 columns

Added 'age_group' and 'income_per_person' columns

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4Train/Test Split

980 rows x 12 columns→Split data into training and testing sets→Training: 784 rows x 12 columns, Testing: 196 rows x 12 columns

80% training and 20% testing split

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5Model Training

784 rows x 12 columns→Train model on training data→Trained model object

Random Forest model trained on training set

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6Model Evaluation

Trained model, 196 rows x 12 columns test data→Evaluate model performance on test data→Accuracy, precision, recall scores

Model accuracy = 85%

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7Prediction

New data 10 rows x 12 columns→Use trained model to predict outcomes→Predictions array of length 10

Predicted customer churn: [0,1,0,0,1,1,0,0,1,0]

Training Trace - Epoch by Epoch

Epoch 1: 0.65 #######
Epoch 2: 0.50 #####
Epoch 3: 0.40 ####
Epoch 4: 0.35 ###
Epoch 5: 0.30 ##

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.65	0.60	Model starts learning with moderate accuracy
2	0.50	0.72	Loss decreases and accuracy improves
3	0.40	0.80	Model continues to improve
4	0.35	0.83	Training converging with better accuracy
5	0.30	0.86	Final epoch with best performance

Prediction Trace - 4 Layers

Layer 1: Input new data sample

Layer 2: Feature scaling

Layer 3: Model prediction

Layer 4: Thresholding

Model Quiz - 3 Questions

What happens to data during the 'Data Cleaning' stage?

A Train the model

B Split data into train and test

C Remove missing or incorrect data

D Make predictions on new data

Key Insight

A clear ML project structure helps organize data and model steps. It ensures data flows logically from raw input to predictions, and training improves model performance steadily.